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Value from Data-Driven Organizations: How Pharmaceuticals are Moving Forward

Posted March 22, 2016

A recent white paper from IBM highlights the importance of data in helping the global healthcare industry meets its goals of reducing overall costs and improving clinical outcomes. The report’s authors stress why organizations must become data-driven if they are to meet these objectives and thrive in the coming era of value-based payments and accountable care. Further, they explain how organizations can become more data-driven through a series of steps.

Here’s how the report describes the end game:

At the core of a data-driven healthcare organization is the ability to analyze a wide range of big data, from within and outside its four walls … Big data comprises much larger volumes, wider varieties and greater velocities of data than most organizations have previously captured, stored and analyzed. It includes data from traditional sources such as electronic medical records (EMRs) and from nontraditional sources such as social media and public health records.

While the paper is not directly addressed to the pharmaceutical industry, the recommended steps can be easily applied to pharmacovigilance and drug safety, clinical research design, research, new formulation development and innovation. These are all organizations that are coming to terms with Big Data and figuring out how data-driven mindsets and processes can improve performance against many different metrics.

Here’s our quick take on a few of the most powerful ideas pharmas can use as they effect their own transformation to become truly data-driven organizations:

Define all data sources

Most pharmaceuticals have loads of data, from both internal and external data sources. But they may not know what they have. The initial step of inventorying current information assets is necessary to determine the best ways to use data. The report’s recommendations are very much on track, as they involve knowing “what can be learned from each potential source” and “whether combining two or more sources of data will yield more insights than if data is analyzed separately.”

We believe the answer to the second question is usually “yes.” Multi-sourced data is increasingly important in the drug safety world as it provides more nuanced insights and more powerful surveillance capabilities for drugs already on the market. The value of social media streams, electronic medical records and claims data are helping drug safety experts to gain clearer and quicker view into potential adverse effects. Through programs such as the EMA’s WEB-RADR and the FDA’s Sentinel Initiative, regulators have signaled their belief that so-called secondary data sets are useful in improving drug safety.

Integrate data sources

This may sound somewhat obvious, but many pharmas still struggle with their heritage of siloed operations. That leads to incompatible systems and disconnected, standalone data repositories. But the benefits of sharing – or at least providing centralized access to – accurate, timely and comprehensive data sets is too valuable to let organizational barriers and IT complexity stand in the way.

Recognizing that different data formats need to be mixed and matched, CIOs are looking for “platform” tools that provide access to many different data types through intuitive interfaces. It’s not a matter of getting all data into one giant data warehouse so much as it is providing the right users the right tools and interfaces so they can engage and interact with integrated sets of data. Ideally, that’s a single platform that allows for cross-referencing diverse data in real time.

Identify analytics needs

This is largely a matter of defining the right questions to ask. For innovation and development teams, the question may be “which drugs in our existing portfolio might be good candidates for repurposing in terms or unmet needs or new indications?” Researchers and designers of clinical trials will ask, “which population groups are most important to test?” PV leaders will seek to know more definitively how medications perform in the real world.

While the IBM article does not address cultural matters, we believe that’s another important factor. New data types and advanced algorithms will have a significant impact on specific parts of the pharmaceutical enterprise. For instance, drug safety and pharmacovigilance teams will augment proven research techniques with big data analytics and business intelligence techniques, natural language processing and machine learning. These are all powerful tools that can help skilled researchers and analysts do their jobs more effectively. Visualizations will also help them uncover powerful new insights.

Understanding the proper role of these capabilities and the best way to use new information assets and technology will also become hallmarks of data-driven pharmaceuticals. In fact, leading organizations will seek to find unique combinations of data and tools and tailor solutions for their specific needs, based on their unique product set. The leaders won’t necessarily be the organizations with the most data, but rather those that have the right processes, teams and toolsets to generate the most intelligence and value from their data.